65 lines
3.1 KiB
Markdown
65 lines
3.1 KiB
Markdown
## Pre-Revision Hints
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- Write the pseudocode or steps to find the HOG feature.
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- What happens if you change the HOG feature from 2x2 to 4x4.
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## Exam Info
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- Don't be misled by previous years.
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- Previously 2/3, now 4/5.
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- More questions, same time, less time per question.
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- Q1 is compulsory.
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- More options as there is more to cover.
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- Do one of two coding tasks.
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- More graphics on 2021/2022 paper than our one.
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## Exam hints
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- Probably won't be asked for algorithms, but could be asked for general steps.
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- What are steps when detecting faces, steps to follow to compute HOG of descriptor, steps for camera calibration.
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- Very small computations, like how to compute the gradient for a particular pixel in x direction, y direction, etc.
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- One could be canvas 2d, one could be threejs, no guarantee that there won't be two on the same technology.
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- Can't get away with just studying one technology.
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- Theory questions and application questions.
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- Doing analysis on an image.
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- Won't be too theoretical, going into mathematics, will be more applied.
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- One is around camera calibration and the other around feature and descriptors.
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- We did basics like calculating differentials.
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- These will be the ideas around Q4 and Q5.
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- Won't be calculating eigenvalues.
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- Basic mathematics.
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- Compute the total amount of features you have in HOG.
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- Don't focus on solving mathematics for camera calibration, just need to understand how you create an intrinsic matrix given focal length etc.
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- Construct K-matrix.
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- Anatomy of the camera matrix.
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- What the rows of the P-matrix means.
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- Feature side:
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- Focus on steps need to follow, why you need to.
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- Benefit of principal components.
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- Just have to do one image analysis question.
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- Try not to just pick one, the other one could be difficult.
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- Exam won't be easy.
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- Make sure you understand concepts covered in class.
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- Waqar's part is more novel, so he has given us extra info about exam.
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- Nasre's stuff is more overlapping from previous years.
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- Will be given useful methods to refer to, like in Sam's papers.
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- Answer can be derived from methods given.
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- For both Canvas and ThreeJS.
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- Definition of surface normal for example, can give formula or describe.
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- Nasre tends to be more applied: might ask more applied questions that theoretical questions.
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- Analyse this image, write this code.
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- Perfect answer for erosion: no equation, description, example.
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- We didn't do image de-calibration.
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- Hough transform?
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- Or adaptive thresholding?
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- Think in transforms, draw transforms.
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- Nasre really likes transforms.
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- It's good to look at previous exam papers.
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- Possibly taken question from previous years???
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- Was answering question.
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- Look at last 3/4 exam papers.
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- Wouldn't pass if you only studied last year's paper.
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- Try to remember kernel matrices for filters, e.g., smoothing etc.
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- Nasre wants us to know the matrix values.
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- Can learn how to develop kernel from first principles.
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- Here is a one directional filter, make a two directional one.
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- like how you make laplacian of gaussian.
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- How to convolve filters.
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